code: def _derive_shapelet_distances(X, shapelet):
      """Derive the distance between a shapelet and all the time series."""
      n_samples, n_windows, _ = X.shape
      mean = np.empty((n_samples, n_windows))
      for i in prange(n_samples):
          for j in prange(n_windows):
              mean[i, j] = np.mean((X[i, j] - shapelet) ** 2)
      dist = np.empty(n_samples)
      for i in prange(n_samples):
          dist[i] = np.min(mean[i])
      return dist
  
imports: from itertools import chain
  from joblib import delayed, Parallel
  from numba import njit, prange
  from numba.typed import List
  import numpy
  from numpy.lib.stride_tricks import as_strided
  from sklearn.base import BaseEstimator
  from sklearn.feature_selection import f_classif, mutual_info_classif
  from sklearn.utils.validation import check_array, check_is_fitted, check_random_state, check_X_y
  from ..base import UnivariateTransformerMixin
  from ..utils.utils import _windowed_view
label_desc: Use image node_id 2 for calling a global function with example usage: _derive_shapelet_distances(X, shapelet) and returns: dist